Senior ML Engineer Infrastructure & Data Systems

Arbisoft; Lahore; On-site; PKR 2,000,000 - 3,500,000; Full time

Job summary

Arbisoft is seeking a Senior Machine Engineer to build, ship, and maintain production software with a focus on cutting‑edge AI solutions. You will own CI/CD pipelines, deployment configurations for Docker Swarm and Kubernetes, and robust ML/DL model evaluation.

The role emphasizes infrastructure, reliability, and scalable testing in a production environment. The ideal candidate has about 5 years of hands‑on experience with Python, transformers, and cloud‑based ML systems, plus strong

Qualifications

• 5+ years of industry experience building and shipping software.
• Degree in Computer Science or related field.
• Strong CI/CD and cloud infrastructure experience.

Responsibilities

• Build and maintain CI/CD pipelines and deployment configs for Docker Swarm and Kubernetes.
• Evaluate ML/DL models using PyTorch, TensorFlow, or similar frameworks.
• Develop and optimize LLM-based systems, including prompt tuning and adapter training.
• Own infrastructure automation and DevOps practices across environments.
• Set up monitoring for services in production and write scalable tests.
• Address technical debt and improve messy codebases.
• Work with SQL and data processing tasks as needed.

Skills

CI/CD workflows

GitHub Actions

Docker

Kubernetes

PyTorch

TensorFlow

LLM systems

RAG pipelines

Embeddings

Vector databases

SQL

DevOps practices

Testing frameworks

Code debugging

Education

BS in Computer Science

Masters in Computer Science

Tools

FAISS

Pinecone

Weaviate

Job description

We're looking for a Senior Machine Engineer with around 5 years of hands‑on experience building, shipping, and maintaining production software, and deploying cutting‑edge AI solutions. The ideal candidate brings deep expertise in Python, transformers, and scalable cloud‑based ML systems. This is an engineering‑first role; the majority of your time will go into infrastructure, CI/CD, and platform reliability.

What you’ll be doing

• Building and maintaining CI/CD pipelines using GitHub Actions
• Designing and managing deployment configurations for Docker Swarm and Kubernetes
• Evaluate ML/DL models using PyTorch, TensorFlow, or similar frameworks
• Build and optimize LLM‑based systems, including prompt‑tuning, fine‑tuning, and adapter‑based training (e.g., LoRA, QLoRA)
• Owning infrastructure automation and applying solid DevOps practices across environments
• Setting up and maintaining monitoring for services running in production
• Writing automated tests and building testing frameworks that scale with the codebase
• Troubleshooting and maintaining complex, sometimes messy codebases and improving them
• Actively identifying and resolving technical debt
• Working with SQL and handling data processing tasks as needed
• Occasionally training simple ML models (e.g. regression models in PyTorch)
• Can develop robust and scalable RAG pipelines. In-depth knowledge of embeddings and can work with vector databases like FAISS, Pinecone, Weaviate, etc.
• Communicating clearly and consistently with engineers, data scientists, product managers, and business stakeholders

What we’re looking for

• 5 years of industry experience building and shipping software
• BS in Computer Science or masters
• Strong command of CI/CD workflows, GitHub Actions, Docker, and infrastructure automation
• Solid experience with Kubernetes and Docker Swarm deployment setups
• Good working knowledge of SQL and data processing
• Strong code engineering fundamentals, can read, debug, and maintain codebases you didn’t write
• A track record of dealing with tech debt, not just avoiding it
• Excellent communication skills who can explain technical tradeoffs to non‑technical stakeholders and stay aligned with cross‑functional teams over time

Communication & Ownership Expectations

Once a task is assigned, you’re expected to own it end‑to‑end:

• Understand the task fully and clarify uncertainties upfront
• Create and maintain a clear ticket, aligned with the team on scope
• Set an estimate once scoped
• Flag blockers early and communicate delays as soon as they arise
• Let the team know if you finish early
• Share at least a weekly update, more often when there are significant changes